Reversed-direction 2-point modelling applied to divertor conditions in DIII-D <sup>*</sup>
Bibliographic record
Abstract
Abstract A predictive form of the extended 2-point model known as the ‘reverse 2-point model’, Rev2PM, is applied to a range of detachment levels in the open lower divertor of DIII-D, showing that the experimentally measured electron temperature (Te ) and pressure (pe ) at the divertor entrance can be calculated within 50% from target measurements, if and only if a posteriori corrections for convective heat flux are included in the model. Unlike the standard 2-point model, the Rev2PM calculates upstream scrape-off layer (SOL) quantities (such as separatrix Te and pe ) from target conditions (such as Te and parallel heat flux), with volumetric power and momentum losses depending solely on target Te . The Rev2PM is tested against a database of DIII-D inter-ELM divertor Thomson scattering measurements, built from a series of 6 MW, 1.3 MA, LSN H-mode discharges with varied main ion density, drift direction, and nitrogen puffing rate. Measured target Te ranged from 0.4–25 eV over this database, and upstream Te ranged from 5–60 eV. Poor agreement is found between upstream measurements and Rev2PM calculations that assume purely conductive parallel heat transport. However, introducing a posteriori corrections to account for convective heat transport brings the Rev2PM calculations within 50% of the measured upstream values across the dataset. These corrections imply that up to 99% of the parallel heat flux is carried by convection in detached conditions in the DIII-D open lower divertor, though further work is required to assess any potential dependencies on device size or divertor closure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".